A Fermentation State Marker Rule Design Task in Metabolic Engineering.

There are several ways in which mathematical modeling is used in fermentation control, but mechanistic mathematical genome-scale models of metabolism within the cell have not been applied or implemented so far. As part of the metabolic engineering task setting, we propose that metabolite fluxes and/or biomass growth rate be used to search for a fermentation steady state marker rule. During fermentation, the bioreactor control system can automatically detect the desired steady state using a logical marker rule. The marker rule identification can be also integrated with the production growth coupling approach, as presented in this study. A design of strain with marker rule is demonstrated on genome scale metabolic model iML1515 of Escherichia coli MG1655 proposing two gene deletions enabling a measurable marker rule for succinate production using glucose as a substrate. The marker rule example at glucose consumption 10.0 is: IF (specific growth rate µ is above 0.060 h-1, AND CO2 production under 1.0, AND ethanol production above 5.5), THEN succinate production is within the range 8.2-10, where all metabolic fluxes units are mmol ∗ gDW-1 ∗ h-1. An objective function for application in metabolic engineering, including productivity features and rule detecting sensor set characterizing parameters, is proposed. Two-phase approach to implementing marker rules in the cultivation control system is presented to avoid the need for a modeler during production.

OptEnvelope: A target point guided method for growth-coupled production using knockouts.

Finding the best knockout strategy for coupling biomass growth and production of a target metabolite using a mathematic model of metabolism is a challenge in biotechnology. In this research, a three-step method named OptEnvelope is presented based on finding minimal set of active reactions for a target point in the feasible solution space (envelope) using a mixed-integer linear programming formula. The method initially finds the reduced desirable solution space envelope in the product versus biomass plot by removing all inactive reactions. Then, with reinsertion of the deleted reactions, OptEnvelope attempts to reduce the number of knockouts so that the desirable production envelope is preserved. Additionally, OptEnvelope searches for envelopes with higher minimum production rates or fewer knockouts by evaluating different target points within the desired solution space. It is possible to limit the maximal number of knockouts. The method was implemented on metabolic models of E. coli and S. cerevisiae to test the method benchmarking the capability of these industrial microbes for overproduction of acetate and glycerol under aerobic conditions and succinate and ethanol under anaerobic conditions. The results illustrate that OptEnvelope is capable to find multiple strong coupled envelopes located in the desired solution space because of its novel target point oriented strategy of envelope search. The results indicate that E. coli is more appropriate to produce acetate and succinate while S. cerevisiae is a better host for glycerol production. Gene deletions for some of the proposed reaction knockouts have been previously reported to increase the production of these metabolites in experiments. Both organisms are suitable for ethanol production, however, more knockouts for the adaptation of E. coli are required. OptEnvelope is available at https://github.com/lv-csbg/optEnvelope.

Closing the loop in bioproduction: Spent microbial biomass as a resource within circular bioeconomy.

Successful transition to a circular bioeconomy relies on the availability and efficient use of organic feedstocks such as agricultural and food waste. Advances in industrial biotechnology provide novel tools to valorize these feedstocks differently. Less attention, however, has been directed towards assessment of the organic side-residues arising from industrial biotechnology, such as spent microbial biomass (SMB). This study aims to reflect the current state of SMB within bioeconomy and create awareness of this growing industrial resource. Data from a range of published fermentation processes is used to estimate the amount of SMB formed per product (weight per weight, wt/wt) across different types of bioproducts, namely organic acids, alcohols, polymers, amino acids, antibiotics, protein and vitamins. Varying amounts of SMB are generated depending on the bioproducts and bioprocess, where bulk bioproducts, e.g. alcohols, generate less SMB than bioproduction of high-value low-volume specialty products, e.g. vitamins. It is estimated that more than 50 million tons of nutrient-rich SMB was generated in 2013, with SMB from bulk and specialty bioproduction accounting for roughly equal amounts. Furthermore, the composition of six industrially relevant organisms is summarized and compared, highlighting the general features of SMB as a carbon-rich substrate mainly consisting of protein. The results indicate that SMB is a growing resource with a reliable supply and predictable composition. The predictable nature of SMB could make it a favorable substrate for further innovation in industrial applications and nutrient circulation within the bioeconomy, for example, by using it as a co-substrate for valorization of other biomasses.

Kinetic and Stoichiometric Modeling-Based Analysis of Docosahexaenoic Acid (DHA) Production Potential by Crypthecodinium cohnii from Glycerol, Glucose and Ethanol.

Docosahexaenoic acid (DHA) is one of the most important long-chain polyunsaturated fatty acids (LC-PUFAs), with numerous health benefits. Crypthecodinium cohnii, a marine heterotrophic dinoflagellate, is successfully used for the industrial production of DHA because it can accumulate DHA at high concentrations within the cells. Glycerol is an interesting renewable substrate for DHA production since it is a by-product of biodiesel production and other industries, and is globally generated in large quantities. The DHA production potential from glycerol, ethanol and glucose is compared by combining fermentation experiments with the pathway-scale kinetic modeling and constraint-based stoichiometric modeling of C. cohnii metabolism. Glycerol has the slowest biomass growth rate among the tested substrates. This is partially compensated by the highest PUFAs fraction, where DHA is dominant. Mathematical modeling reveals that glycerol has the best experimentally observed carbon transformation rate into biomass, reaching the closest values to the theoretical upper limit. In addition to our observations, the published experimental evidence indicates that crude glycerol is readily consumed by C. cohnii, making glycerol an attractive substrate for DHA production.

Metformin Transport Rates Between Plasma and Red Blood Cells in Humans.

BACKGROUND: Metformin has been used for the treatment of type 2 diabetes for over 60 years; however, its mechanism of pharmacological action is not fully clear. Different hypotheses exist regarding metformin distribution and redistribution mechanisms between plasma and erythrocytes/red blood cells (RBCs). OBJECTIVE: We aimed to test the hypothesis that the metformin distribution between plasma and RBC occurs via concentration difference-driven passive transport and estimated transport rate coefficient values based on metformin concentration time series in plasma and RBCs from in vivo studies. METHODS: An ordinary differential equation (ODE) system with two compartments was used to describe diffusion-based passive transport between plasma and RBCs. Metformin concentration time series in plasma and RBCs of 35 individuals were used for metformin transport parametrization. Plasma concentration has been approximated by biexponential decline. RESULTS: A single passive transport coefficient, k = 0.044 ± 0.014 (h-1), can be applied, describing the uptake and release transport rate versus the linear equation v = k × (Mpl - MRBC), where Mpl is the metformin concentration in plasma and MRBC is the metformin concentration in RBCs. CONCLUSIONS: Our research suggests that passive transport can explain metformin distribution dynamics between plasma and RBCs because transport speed is proportional to the metformin concentration difference and independent of the transport direction. Concentration difference-driven passive transport can explain the mechanism of faster metformin distribution to RBCs the first few hours after administration, and faster release and domination of the redistribution transport rate after metformin concentration in plasma becomes smaller than in RBCs.

Sustainable metabolic engineering for sustainability optimisation of industrial biotechnology.

Industrial biotechnology represents one of the most innovating and labour-productive industries with an estimated stable economic growth, thus giving space for improvement of the existing and setting up new value chains. In addition, biotechnology has clear environmental advantages over the chemical industry. Still, biotechnology's environmental contribution is sometimes valued with controversy and societal aspects are frequently ignored. Environmental, economic and societal sustainability of various bioprocesses becomes increasingly important due to the growing understanding about complex and interlinked consequences of different human activities. Neglecting the sustainability issues in the development process of novel solutions may lead to sub-optimal biotechnological production, causing adverse environmental and societal problems proportional to the production volumes. In the paper, sustainable metabolic engineering (SME) concept is proposed to assess and optimize the sustainability of biotechnological production that can be derived from the features of metabolism of the exploited organism. The SME concept is optimization of metabolism where economic, environmental and societal sustainability parameters of all incoming and outgoing fluxes and produced biomass of the applied organisms are considered. The extension of characterising features of strains designed by metabolic engineering methods with sustainability estimation enables ab initio improvement of the biotechnological production design.

IMFLer: A Web Application for Interactive Metabolic Flux Analysis and Visualization.

Increasing genome-wide data in biological sciences and medicine has contributed to the development of a variety of visualization tools. Several automatic, semiautomatic, and manual visualization tools have already been developed. Some even have integrated flux balance analysis (FBA), but in most cases, it depends on separately installed third party software that is proprietary and does not allow customization of its functionality and has many restrictions for easy data distribution and analysis. In this study, we present an interactive metabolic flux analyzer and visualizer (IMFLer)-a static single-page web application that enables the reading and management of metabolic model layout maps, as well as immediate visualization of results from both FBA and flux variability analysis (FVA). IMFLer uses the Escher Builder tool to load, show, edit, and save metabolic pathway maps. This makes IMFLer an attractive and easily applicable tool with a user-friendly interface. Moreover, it allows to faster interpret results from FBA and FVA and improves data interoperability by using a standardized file format for the genome-scale metabolic model. IMFLer is a fully open-source tool that enables the rapid visualization and interpretation of the results of FBA and FVA with no time setup and no programming skills required, available at https://lv-csbg.github.io/IMFLer/.

Syntrophy of Crypthecodinium cohnii and immobilized Zymomonas mobilis for docosahexaenoic acid production from sucrose-containing substrates.

Marine heterotrophic dinoflagellate Crypthecodinium cohnii is an aerobic oleaginous microorganism that accumulates intracellular lipid with high content of 4,7,10,13,16,19-docosahexaenoic acid (DHA), a polyunsaturated ω-3 (22:6) fatty acid with multiple health benefits. C. cohnii can grow on glucose and ethanol, but not on sucrose or fructose. For conversion of sucrose-containing renewables to C. cohnii DHA, we investigated a syntrophic process, involving immobilized cells of ethanologenic bacterium Zymomonas mobilis for fermenting sucrose to ethanol. The non-respiring, NADH dehydrogenase-deficient Z. mobilis strain Zm6-ndh, with high ethanol yield both under anaerobic and aerobic conditions, was taken as the genetic background for inactivation of levansucrase (sacB). SacB mutation eliminated the levan-forming activity on sucrose. The double mutant Zm6-ndh-sacB cells were immobilized in Ca alginate, and applied for syntrophic conversion of sucrose to DHA of C. cohnii, either taking the ethanol-containing fermentation medium from the immobilized Z. mobilis for feeding to the C. cohnii fed-batch culture, or directly coculturing the immobilized Zm6-ndh-sacB with C. cohnii on sucrose. Both modes of cultivation produced C. cohnii CCMP 316 biomass with DHA content around 2-3 % of cell dry weight, corresponding to previously reported results for this strain on glucose.

Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues.

Metformin is the primary drug for type 2 diabetes treatment and a promising candidate for other disease treatment. It has significant deviations between individuals in therapy efficiency and pharmacokinetics, leading to the administration of an unnecessary overdose or an insufficient dose. There is a lack of data regarding the concentration-time profiles in various human tissues that limits the understanding of pharmacokinetics and hinders the development of precision therapies for individual patients. The physiologically based pharmacokinetic (PBPK) model developed in this study is based on humans' known physiological parameters (blood flow, tissue volume, and others). The missing tissue-specific pharmacokinetics parameters are estimated by developing a PBPK model of metformin in mice where the concentration time series in various tissues have been measured. Some parameters are adapted from human intestine cell culture experiments. The resulting PBPK model for metformin in humans includes 21 tissues and body fluids compartments and can simulate metformin concentration in the stomach, small intestine, liver, kidney, heart, skeletal muscle adipose, and brain depending on the body weight, dose, and administration regimen. Simulations for humans with a bodyweight of 70kg have been analyzed for doses in the range of 500-1500mg. Most tissues have a half-life (T1/2) similar to plasma (3.7h) except for the liver and intestine with shorter T1/2 and muscle, kidney, and red blood cells that have longer T1/2. The highest maximal concentrations (Cmax) turned out to be in the intestine (absorption process) and kidney (excretion process), followed by the liver. The developed metformin PBPK model for mice does not have a compartment for red blood cells and consists of 20 compartments. The developed human model can be personalized by adapting measurable values (tissue volumes, blood flow) and measuring metformin concentration time-course in blood and urine after a single dose of metformin. The personalized model can be used as a decision support tool for precision therapy development for individuals.

An Early Stage Researcher's Primer on Systems Medicine Terminology.

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.

Community effort endorsing multiscale modelling, multiscale data science and multiscale computing for systems medicine.

Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology (COST) Action CA15120 Open Multiscale Systems Medicine (OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner. This will result in follow-up white papers and open access resources to accelerate the clinical translation of systems medicine.

Misinterpretation risks of global stochastic optimisation of kinetic models revealed by multiple optimisation runs.

One of use cases for metabolic network optimisation of biotechnologically applied microorganisms is the in silico design of new strains with an improved distribution of metabolic fluxes. Global stochastic optimisation methods (genetic algorithms, evolutionary programing, particle swarm and others) can optimise complicated nonlinear kinetic models and are friendly for unexperienced user: they can return optimisation results with default method settings (population size, number of generations and others) and without adaptation of the model. Drawbacks of these methods (stochastic behaviour, undefined duration of optimisation, possible stagnation and no guaranty of reaching optima) cause optimisation result misinterpretation risks considering the very diverse educational background of the systems biology and synthetic biology research community. Different methods implemented in the COPASI software package are tested in this study to determine their ability to find feasible solutions and assess the convergence speed to the best value of the objective function. Special attention is paid to the potential misinterpretation of results. Optimisation methods are tested with additional constraints that can be introduced to ensure the biological feasibility of the resulting optimised design: (1) total enzyme activity constraint (called also amino acid pool constraint) to limit the sum of enzyme concentrations and (2) homeostatic constraint limiting steady state metabolite concentration corridor around the steady state concentrations of metabolites in the original model. Impact of additional constraints on the performance of optimisation methods and misinterpretation risks is analysed.

Model-based metabolism design: constraints for kinetic and stoichiometric models.

The implementation of model-based designs in metabolic engineering and synthetic biology may fail. One of the reasons for this failure is that only a part of the real-world complexity is included in models. Still, some knowledge can be simplified and taken into account in the form of optimization constraints to improve the feasibility of model-based designs of metabolic pathways in organisms. Some constraints (mass balance, energy balance, and steady-state assumption) serve as a basis for many modelling approaches. There are others (total enzyme activity constraint and homeostatic constraint) proposed decades ago, but which are frequently ignored in design development. Several new approaches of cellular analysis have made possible the application of constraints like cell size, surface, and resource balance. Constraints for kinetic and stoichiometric models are grouped according to their applicability preconditions in (1) general constraints, (2) organism-level constraints, and (3) experiment-level constraints. General constraints are universal and are applicable for any system. Organism-level constraints are applicable for biological systems and usually are organism-specific, but these constraints can be applied without information about experimental conditions. To apply experimental-level constraints, peculiarities of the organism and the experimental set-up have to be taken into account to calculate the values of constraints. The limitations of applicability of particular constraints for kinetic and stoichiometric models are addressed.

Total enzyme activity constraint and homeostatic constraint impact on the optimization potential of a kinetic model.

The application of biologically and biochemically relevant constraints during the optimization of kinetic models reduces the impact of suggested changes in processes not included in the scope of the model. This increases the probability that the design suggested by model optimization can be carried out by an organism after implementation of design in vivo. A case study was carried out to determine the impact of total enzyme activity and homeostatic constraints on the objective function values and the following ranking of adjustable parameter combinations. The application of constraints on the model of sugar cane metabolism revealed that a homeostatic constraint caused heavier limitations of the objective function than a total enzyme activity constraint. Both constraints changed the ranking of adjustable parameter combinations: no "universal" constraint-independent top-ranked combinations were found. Therefore, when searching for the best subset of adjustable parameters, a full scan of their combinations is suggested for a small number of adjustable parameters, and evolutionary search strategies are suggested for a large number. Simultaneous application of both constraints is suggested.

SpaceScanner: COPASI wrapper for automated management of global stochastic optimization experiments.

MOTIVATION: Due to their universal applicability, global stochastic optimization methods are popular for designing improvements of biochemical networks. The drawbacks of global stochastic optimization methods are: (i) no guarantee of finding global optima, (ii) no clear optimization run termination criteria and (iii) no criteria to detect stagnation of an optimization run. The impact of these drawbacks can be partly compensated by manual work that becomes inefficient when the solution space is large due to combinatorial explosion of adjustable parameters or for other reasons. RESULTS: SpaceScanner uses parallel optimization runs for automatic termination of optimization tasks in case of consensus and consecutively applies a pre-defined set of global stochastic optimization methods in case of stagnation in the currently used method. Automatic scan of adjustable parameter combination subsets for best objective function values is possible with a summary file of ranked solutions. AVAILABILITY AND IMPLEMENTATION: https://github.com/atiselsts/spacescanner . CONTACT: egils.stalidzans@lu.lv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Search for a Minimal Set of Parameters by Assessing the Total Optimization Potential for a Dynamic Model of a Biochemical Network.

Selecting an efficient small set of adjustable parameters to improve metabolic features of an organism is important for a reduction of implementation costs and risks of unpredicted side effects. In practice, to avoid the analysis of a huge combinatorial space for the possible sets of adjustable parameters, experience-, and intuition-based subsets of parameters are often chosen, possibly leaving some interesting counter-intuitive combinations of parameters unrevealed. The combinatorial scan of possible adjustable parameter combinations at the model optimization level is possible; however, the number of analyzed combinations is still limited. The total optimization potential (TOP) approach is proposed to assess the full potential for increasing the value of the objective function by optimizing all possible adjustable parameters. This seemingly unpractical combination of adjustable parameters allows assessing the maximum attainable value of the objective function and stopping the combinatorial space scanning when the desired fraction of TOP is reached and any further increase in the number of adjustable parameters cannot bring any reasonable improvement. The relation between the number of adjustable parameters and the reachable fraction of TOP is a valuable guideline in choosing a rational solution for industrial implementation. The TOP approach is demonstrated on the basis of two case studies.

Strategies for structuring interdisciplinary education in Systems Biology: an European perspective.

Systems Biology is an approach to biology and medicine that has the potential to lead to a better understanding of how biological properties emerge from the interaction of genes, proteins, molecules, cells and organisms. The approach aims at elucidating how these interactions govern biological function by employing experimental data, mathematical models and computational simulations. As Systems Biology is inherently multidisciplinary, education within this field meets numerous hurdles including departmental barriers, availability of all required expertise locally, appropriate teaching material and example curricula. As university education at the Bachelor's level is traditionally built upon disciplinary degrees, we believe that the most effective way to implement education in Systems Biology would be at the Master's level, as it offers a more flexible framework. Our team of experts and active performers of Systems Biology education suggest here (i) a definition of the skills that students should acquire within a Master's programme in Systems Biology, (ii) a possible basic educational curriculum with flexibility to adjust to different application areas and local research strengths, (iii) a description of possible career paths for students who undergo such an education, (iv) conditions that should improve the recruitment of students to such programmes and (v) mechanisms for collaboration and excellence spreading among education professionals. With the growing interest of industry in applying Systems Biology approaches in their fields, a concerted action between academia and industry is needed to build this expertise. Here we present a reflection of the European situation and expertise, where most of the challenges we discuss are universal, anticipating that our suggestions will be useful internationally. We believe that one of the overriding goals of any Systems Biology education should be a student's ability to phrase and communicate research questions in such a manner that they can be solved by the integration of experiments and modelling, as well as to communicate and collaborate productively across different experimental and theoretical disciplines in research and development.

Modeling of Zymomonas mobilis central metabolism for novel metabolic engineering strategies.

Mathematical modeling of metabolism is essential for rational metabolic engineering. The present work focuses on several types of modeling approach to quantitative understanding of central metabolic network and energetics in the bioethanol-producing bacterium Zymomonas mobilis. Combined use of Flux Balance, Elementary Flux Mode, and thermodynamic analysis of its central metabolism, together with dynamic modeling of the core catabolic pathways, can help to design novel substrate and product pathways by systematically analyzing the solution space for metabolic engineering, and yields insights into the function of metabolic network, hardly achievable without applying modeling tools.

Kinetic modelling of the Zymomonas mobilis Entner-Doudoroff pathway: insights into control and functionality.

Zymomonas mobilis, an ethanol-producing bacterium, possesses the Entner-Doudoroff (E-D) pathway, pyruvate decarboxylase and two alcohol dehydrogenase isoenzymes for the fermentative production of ethanol and carbon dioxide from glucose. Using available kinetic parameters, we have developed a kinetic model that incorporates the enzymic reactions of the E-D pathway, both alcohol dehydrogenases, transport reactions and reactions related to ATP metabolism. After optimizing the reaction parameters within likely physiological limits, the resulting kinetic model was capable of simulating glycolysis in vivo and in cell-free extracts with good agreement with the fluxes and steady-state intermediate concentrations reported in previous experimental studies. In addition, the model is shown to be consistent with experimental results for the coupled response of ATP concentration and glycolytic flux to ATPase inhibition. Metabolic control analysis of the model revealed that the majority of flux control resides not inside, but outside the E-D pathway itself, predominantly in ATP consumption, demonstrating why past attempts to increase the glycolytic flux through overexpression of glycolytic enzymes have been unsuccessful. Co-response analysis indicates how homeostasis of ATP concentrations starts to deteriorate markedly at the highest glycolytic rates. This kinetic model has potential for application in Z. mobilis metabolic engineering and, since there are currently no E-D pathway models available in public databases, it can serve as a basis for the development of models for other micro-organisms possessing this type of glycolytic pathway.